Fig. 3.

The proposed data augmentation. Top/Middle: input (binarized) spherical data (geometric feature) F is deformed to that of a target sphere. After rigid rotation (F⁰), the deformed spherical data become closer as a degree of spherical harmonics increases. Bottom: the manual annotation Z is driven by the intermediate deformation. Even with the improved geometric feature matching, manual annotation does not necessarily match due to spatial inconsistency and various sulcal branches (yellow box). This implies that a single training sample is insufficient to capture variability of sulci in LPFC. For data augmentation, the proposed method utilizes intermediate deformation of the combinatorial registration, by which sulcal variability can be better captured than a single training sample. Thus, model training is generalized by learning neuroanatomical variations provided by manual annotation for a set of similar spherical data (geometric features), to which the enhanced inference of unseen data belongs.